Real-Time Cargo Loading Identification Based on Self-Powered Camera

In practice, the statistics of cargo loading capacity needs to be realized by counting the loading actions. Object detection algorithm may not meet the requirements of such tasks under the influence of environmental factors. And the current temporal action localization methods may ignore the spatial information of objects and identify the inaccurate action time boundary of multiple objects in the cargo loading task. To fill this technical gap, this paper designs a cargo loading identification framework. The core of this framework is the Target Area Rise-descend (TAR) algorithm, which is based on the object detection model and uses the regularity of object location and object size to realize real-time recognition of the cargo loading process. We ensure the real-time and robustness of the algorithm by some means like nap mechanism and time regularity. The garbage truck recycling task is taken as an example to complete the elaboration of the proposed method. Experiments show that the accuracy of the proposed method is higher than 99%, and the real-time performance is higher than 20fps on Neural Network Computing Unit (NPU). Considering the limitations of intelligent application scenarios and resources, self-powered camera is applied. For mechanical motion similar to the scene in this paper, the proposed algorithm can use extremely low computing power to capture the motion process and achieve data statistics.